The broad, long-term goal of this project is to develop novel noninvasive neuroprosthetics for restoration and/or rehabilitation of bipedal locomotion in patients with spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), subcortical stroke or lower limb amputations. The control of bipedal locomotion is of great interest to the fields of brain machine interfaces (BMIs), i.e. devices that utilize neural activity to control limb prosthesis and gait rehabilitation. Since locomotion deficits are commonly associated with SCI and neurodegenerative diseases, there is also a need to investigate new potential therapies to restore gait control in such patients. While the feasibility of a BMI for upper limbs has been demonstrated in studies in monkeys and humans, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This project builds upon findings from non-invasive neural decoding of movements in our laboratory, and follows a principled, step-by-step, experimental and computational approach to neural decoding of human bipedal locomotion from scalp EEG and the development of brain-computer interfaces for gait rehabilitation. The specific aims of this project are: 1) to investigate what gait parameters are best predicted from brain activity acquired with scalp EEG; 2) to examine longitudinally the changes in the cortical representation of gait during adaptation to virtual cortical lesions or virtual perturbations of gait kinematics using a closed-loop BCI environment. This will be the first time-resolved examination of how cortical networks may adapt to changes in the neural representation of gait in healthy subjects, and may have implications for studying cortical plasticity after brain injury or physical disability, and for the development of BMIs for gait restoration. This research is clinically significant to patients with impaired gait function, as in the case of stroke patients, Parkinson's disease, SCI and lower-limb amputees, as BMIs may one day help restore gait function.